Data Science, Manager/ Lead (Insurance, 65k)

Michael Page International (HK) Ltd; Hong Kong; On-site; HKD 900,000 - 1,500,000; Full time

Benefits offered by this job

Insurance industry exposure

Cloud-based analytics deployment

Enterprise-scale transformation

Job summary

Michael Page International (HK) Ltd in Hong Kong seeks a Data Science Tech Lead to guide data science delivery within the insurance sector. You will lead technical work across Technology, ensuring ML solutions are reliable, scalable, and business-ready.

You will drive the DS lifecycle, deploy production ML models at scale, and mentor the team while collaborating with Product, Marketing, and IT to embed analytics into business processes.

Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, CS, Math, or related field.
• Min. 8+ years of advanced analytics, ML, and customer analytics experience.
• Proven track record deploying ML models into production at scale.
• Strong hands-on experience with ML platforms and MLOps/AIOps best practices.
• Experience with feature engineering and large, multi-source datasets.
• Knowledge of model explainability and governance best practices.

Responsibilities

• Lead design, development, and production deployment of ML solutions and automated data pipelines.
• Drive technical excellence across DS lifecycle, including architecture, code quality, and mentorship.
• Perform data prep, feature engineering, and customer segmentation for actionable insights.
• Develop, validate, and optimize predictive models (propensity, churn, future value, risk scoring).
• Establish model monitoring, performance tracking, and drift management.
• Collaborate with Product, Marketing, and Tech to embed analytics into business processes.
• Translate analytical outputs into clear business insights for technical and non-technical stakeholders.
• Enhance model performance through feature innovation and new data sources.
• Coordinate with Cloud, Infra, Cybersecurity, DevOps, and IT for scalable deployments.
• Manage project deliverables, timelines, risks, and stakeholder communications.
• Act as liaison between business and tech to align analytics with strategy.

Skills

Python

SQL

Databricks

MLOps

AIOps

ML platforms

Model governance

SHAP explainability

Education

Bachelor's or Master's in Data Science / related field

Tools

MLflow

Airflow

CI/CD

Docker

Cloud platforms

Job description

As a Data Science Tech Lead in Hong Kong, you will guide data science delivery within the insurance industry and help turn data into practical technology outcomes. You will lead technical work across the Technology department, ensuring solutions are clear, reliable and fit for business use.

The organisation operates in the insurance industry in Hong Kong.

Description

• Lead the design, development, and production deployment of machine learning solutions, automated data pipelines, and advanced customer analytics models.
• Drive technical excellence across the data science lifecycle, including solution architecture, engineering standards, code quality, and team mentorship.
• Perform data preparation, feature engineering, and customer segmentation to generate actionable business insights.
• Develop, validate, and optimize predictive models such as propensity, churn, future value, and risk-scoring models.
• Establish model monitoring frameworks, track performance, and proactively manage model drift and degradation.
• Collaborate with Product, Marketing, and Technology teams to embed analytics into business processes and customer engagement initiatives.
• Translate complex analytical outputs into clear business insights for both technical and non-technical stakeholders.
• Enhance model performance through continuous feature innovation and integration of new data sources.
• Work closely with Cloud, Infrastructure, Cybersecurity, DevOps, and IT teams to ensure scalable, secure, and resilient deployments.
• Manage project deliverables, timelines, milestones, risks, and stakeholder communications.
• Act as a key liaison between business and technical teams to align analytics initiatives with strategic objectives.

Profile

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related discipline.
• Min. 8+ years of experience in advanced analytics, machine learning, predictive modelling, and customer analytics.
• Strong hands-on experience designing and operating ML platforms using MLOps/AIOps best practices.
• Proven track record in deploying machine learning models into production environments at scale.
• Expertise in feature engineering and working with large, complex datasets from multiple sources.
• Strong proficiency in Python (Pandas, Scikit-learn, XGBoost, LightGBM), SQL, and Databricks.
• Experience with ML tools and frameworks such as MLflow, Airflow, CI/CD pipelines, containerization, and cloud platforms.
• Knowledge of model explainability techniques (e.g., SHAP) and model governance best practices.
• Experience building monitoring frameworks, model performance dashboards, and drift detection solutions.

Job Offer

• Insurance (Life & General Insurance) industry exposure.
• Cloud-based analytics and machine learning deployment experience.
• Experience leading enterprise-scale data science transformation initiatives.